Characteristics and determinants of personal exposure to PM2.5 mass and components in adult subjects in the megacity of Guangzhou, China

Xiao-Cui Chen; Heiko J. Jahn; Tony J. Ward; Hung Chak Ho; Ming Luo; Guenter Engling; Alexander Kraemer

doi:10.1016/j.atmosenv.2020.117295

Characteristics and determinants of personal exposure to PM_2.5 mass and components in adult subjects in the megacity of Guangzhou, China

Xiao-Cui Chen^*, Heiko J. Jahn, Tony J. Ward^*, Hung Chak Ho, Ming Luo, Guenter Engling, Alexander Kraemer

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

14 Citations (Scopus)

Abstract

Understanding the heterogeneity between ambient concentration and personal exposure is crucial in studies regarding the health risks of air pollution exposure. We performed a panel study with 4–19 (average = 10) repeated personal monitoring in 16 adult subjects (ages 18–30) for three consecutive weeks during the winter and summer of 2011–2012 in the Chinese megacity of Guangzhou. Also, we conducted simultaneous ambient measurements at eight districts (including five urban sites, two suburban locations, and one rural site) of Guangzhou. Significant seasonal variations were shown in personal PM_2.5 exposure for most of the analyzed components (p < 0.05), with higher levels in winter than in summer. Average personal exposures exhibited a pattern of central urban > suburban > rural areas for PM_2.5 mass and most of the constituents (e.g., carbonaceous aerosols, ions). We applied mixed-effects models to estimate within- and between-subject variance components and determinants of personal PM_2.5 exposure after adjusting for potential confounders. The within-subject variance component dominated the total variability (63.7–95.6%) for most of the investigated PM_2.5 components. Ambient PM_2.5 mass and its components were the dominant predictors and contributors of the corresponding personal exposures (0.11 < R_c² < 0.97; p < 0.05). The results indicate that season and district type affect personal PM_2.5 exposure and its components, contributing to 4.9–51.6% and 8.0–77.8% of the variability. Time indoors and outdoors were also factors affecting personal exposure. The study findings revealed ambient concentrations at a fixed monitoring station underestimated residents’ true exposure levels. In conclusion, the current study emphasizes the need for incorporating spatio-temporal activity patterns complementing evenly-distributed air quality monitoring networks to increase the estimation power in epidemiological analysis linking true personal exposure to health effects. © 2020 Elsevier Ltd

Original language	English
Article number	117295
Journal	Atmospheric Environment
Volume	224
Online published	18 Jan 2020
DOIs	https://doi.org/10.1016/j.atmosenv.2020.117295
Publication status	Published - 1 Mar 2020
Externally published	Yes

Funding

Xiao-Cui Chen acknowledges the National Natural Science Foundation of China (Grant No.: 41907181 ). This study was funded by the German Research Foundation (DFG) (Grant KR 947/10-1 , 2, 3) within the framework of the Priority Program 1233 “Megacities – Mega challenge: Informal Dynamics of Global Change”. We are grateful to the subjects who participated in this study and acknowledge the contribution of the research team members.

Research Keywords

Mixed-effects model
Personal exposure
PM2.5 constituents
Within-subject variability

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@article{11e42c160b1744eb83ab53e0b740e837,

title = "Characteristics and determinants of personal exposure to PM2.5 mass and components in adult subjects in the megacity of Guangzhou, China",

abstract = "Understanding the heterogeneity between ambient concentration and personal exposure is crucial in studies regarding the health risks of air pollution exposure. We performed a panel study with 4–19 (average = 10) repeated personal monitoring in 16 adult subjects (ages 18–30) for three consecutive weeks during the winter and summer of 2011–2012 in the Chinese megacity of Guangzhou. Also, we conducted simultaneous ambient measurements at eight districts (including five urban sites, two suburban locations, and one rural site) of Guangzhou. Significant seasonal variations were shown in personal PM2.5 exposure for most of the analyzed components (p < 0.05), with higher levels in winter than in summer. Average personal exposures exhibited a pattern of central urban > suburban > rural areas for PM2.5 mass and most of the constituents (e.g., carbonaceous aerosols, ions). We applied mixed-effects models to estimate within- and between-subject variance components and determinants of personal PM2.5 exposure after adjusting for potential confounders. The within-subject variance component dominated the total variability (63.7–95.6%) for most of the investigated PM2.5 components. Ambient PM2.5 mass and its components were the dominant predictors and contributors of the corresponding personal exposures (0.11 < Rc2 < 0.97; p < 0.05). The results indicate that season and district type affect personal PM2.5 exposure and its components, contributing to 4.9–51.6% and 8.0–77.8% of the variability. Time indoors and outdoors were also factors affecting personal exposure. The study findings revealed ambient concentrations at a fixed monitoring station underestimated residents{\textquoteright} true exposure levels. In conclusion, the current study emphasizes the need for incorporating spatio-temporal activity patterns complementing evenly-distributed air quality monitoring networks to increase the estimation power in epidemiological analysis linking true personal exposure to health effects. {\textcopyright} 2020 Elsevier Ltd",

keywords = "Mixed-effects model, Personal exposure, PM2.5 constituents, Within-subject variability",

author = "Xiao-Cui Chen and Jahn, {Heiko J.} and Ward, {Tony J.} and Ho, {Hung Chak} and Ming Luo and Guenter Engling and Alexander Kraemer",

year = "2020",

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doi = "10.1016/j.atmosenv.2020.117295",

language = "English",

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T1 - Characteristics and determinants of personal exposure to PM2.5 mass and components in adult subjects in the megacity of Guangzhou, China

AU - Chen, Xiao-Cui

AU - Jahn, Heiko J.

AU - Ward, Tony J.

AU - Ho, Hung Chak

AU - Luo, Ming

AU - Engling, Guenter

AU - Kraemer, Alexander

PY - 2020/3/1

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N2 - Understanding the heterogeneity between ambient concentration and personal exposure is crucial in studies regarding the health risks of air pollution exposure. We performed a panel study with 4–19 (average = 10) repeated personal monitoring in 16 adult subjects (ages 18–30) for three consecutive weeks during the winter and summer of 2011–2012 in the Chinese megacity of Guangzhou. Also, we conducted simultaneous ambient measurements at eight districts (including five urban sites, two suburban locations, and one rural site) of Guangzhou. Significant seasonal variations were shown in personal PM2.5 exposure for most of the analyzed components (p < 0.05), with higher levels in winter than in summer. Average personal exposures exhibited a pattern of central urban > suburban > rural areas for PM2.5 mass and most of the constituents (e.g., carbonaceous aerosols, ions). We applied mixed-effects models to estimate within- and between-subject variance components and determinants of personal PM2.5 exposure after adjusting for potential confounders. The within-subject variance component dominated the total variability (63.7–95.6%) for most of the investigated PM2.5 components. Ambient PM2.5 mass and its components were the dominant predictors and contributors of the corresponding personal exposures (0.11 < Rc2 < 0.97; p < 0.05). The results indicate that season and district type affect personal PM2.5 exposure and its components, contributing to 4.9–51.6% and 8.0–77.8% of the variability. Time indoors and outdoors were also factors affecting personal exposure. The study findings revealed ambient concentrations at a fixed monitoring station underestimated residents’ true exposure levels. In conclusion, the current study emphasizes the need for incorporating spatio-temporal activity patterns complementing evenly-distributed air quality monitoring networks to increase the estimation power in epidemiological analysis linking true personal exposure to health effects. © 2020 Elsevier Ltd

AB - Understanding the heterogeneity between ambient concentration and personal exposure is crucial in studies regarding the health risks of air pollution exposure. We performed a panel study with 4–19 (average = 10) repeated personal monitoring in 16 adult subjects (ages 18–30) for three consecutive weeks during the winter and summer of 2011–2012 in the Chinese megacity of Guangzhou. Also, we conducted simultaneous ambient measurements at eight districts (including five urban sites, two suburban locations, and one rural site) of Guangzhou. Significant seasonal variations were shown in personal PM2.5 exposure for most of the analyzed components (p < 0.05), with higher levels in winter than in summer. Average personal exposures exhibited a pattern of central urban > suburban > rural areas for PM2.5 mass and most of the constituents (e.g., carbonaceous aerosols, ions). We applied mixed-effects models to estimate within- and between-subject variance components and determinants of personal PM2.5 exposure after adjusting for potential confounders. The within-subject variance component dominated the total variability (63.7–95.6%) for most of the investigated PM2.5 components. Ambient PM2.5 mass and its components were the dominant predictors and contributors of the corresponding personal exposures (0.11 < Rc2 < 0.97; p < 0.05). The results indicate that season and district type affect personal PM2.5 exposure and its components, contributing to 4.9–51.6% and 8.0–77.8% of the variability. Time indoors and outdoors were also factors affecting personal exposure. The study findings revealed ambient concentrations at a fixed monitoring station underestimated residents’ true exposure levels. In conclusion, the current study emphasizes the need for incorporating spatio-temporal activity patterns complementing evenly-distributed air quality monitoring networks to increase the estimation power in epidemiological analysis linking true personal exposure to health effects. © 2020 Elsevier Ltd

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